Simplification of complex DNA profiles using front end cell separation and probabilistic modeling
| Autor: | Susan A. Greenspoon, Nancy A. Stokes, Christopher J. Ehrhardt, Cristina Stanciu, Emily R. Brocato |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Fraction (chemistry) Cell Separation Real-Time Polymerase Chain Reaction Article Antibodies Fluorescence Pathology and Forensic Medicine Flow cytometry 03 medical and health sciences 0302 clinical medicine Labelling Genetics Cell separation medicine Humans 030216 legal & forensic medicine Models Statistical medicine.diagnostic_test Chemistry Probabilistic logic DNA Cell Fraction Cell sorting Flow Cytometry DNA Fingerprinting 030104 developmental biology DNA profiling Molecular Probes Biological system Blood Chemical Analysis Microsatellite Repeats |
| Zdroj: | Forensic Science International: Genetics. 36:205-212 |
| ISSN: | 1872-4973 |
| DOI: | 10.1016/j.fsigen.2018.07.004 |
| Popis: | Forensic samples comprised of cell populations from multiple contributors often yield DNA profiles that can be extremely challenging to interpret. This frequently results in decreased statistical strength of an individual’s association to the mixture and the loss of probative data. The purpose of this study was to test a front-end cell separation workflow on complex mixtures containing as many as five contributors. Our approach involved selectively labelling certain cell populations in dried whole blood mixture samples with fluorescently labeled antibody probe targeting the HLA-A*02 allele, separating the mixture using Fluorescence Activated Cell Sorting (FACS) into two fractions that are enriched in A*02 positive and A*02 negative cells, and then generating DNA profiles for each fraction. We then tested whether antibody labelling and cell sorting effectively reduced the complexity of the original cell mixture by analyzing STR profiles quantitatively using the probabilistic modeling software, TrueAllele® Casework. Results showed that antibody labelling and FACS separation of target populations yielded simplified STR profiles that could be more easily interpreted using conventional procedures. Additionally, TrueAllele® analysis of STR profiles from sorted cell fractions increased statistical strength for the association of most of the original contributors interpreted from the original mixtures. |
| Databáze: | OpenAIRE |
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